12 Difference Between Database System And Data Warehouse


 What Is A Database System?

A database system is an organized collection of data, generally stored and accessed electronically from a computer system.  A database system represents some aspects of the real world and is designed to be build and be populated with data for certain tasks. A database system contains information organized in columns, rows and tables that is periodically indexed to make accessing relevant information more flexible.

What Is A Data Warehouse?

Data warehouse also referred to as enterprise data warehouse is an aggregate of corporate information and data derived from both operational systems and external sources.  A data warehouse is designed to support business decisions by allowing data aggregation, analysis and reporting at different stages.

In data warehouse from different sources is extracted into a single area, transformed according to the decision support system needs and store into a warehouse so as to contribute to future decision making.

Also Read: Difference Between Operational And Information System

Difference Between Database System And Data Warehouse In Tabular Form

Description A database system contains information organized in columns, rows and tables that is periodically indexed to make accessing relevant information more flexible. A data warehouse is a system that pulls together data from many different sources within an organization for reporting and analysis.
Purpose A database is built to store current transactions and enable fast access to specific transactions for ongoing business processes, known as Online Transaction Processing (OLTP). A data warehouse is built to store large quantities of historical data and enable fast, complex queries across all data, typically using (Online Analytical Processing (OLAP).
Data Data within operational database systems are generally updated regularly.   Data within a data warehouse is non-volatile, meaning when data is added old data is not erased so rarely updates.
Concurrent Users A data base system is able to handle thousands of users simultaneously without affecting the system performance. Data warehouse supports a limited number of concurrent users.
Applications With databases, there is one-to-one relationship with a single application as its source. With data warehouses, there is one-to-many relationships between a data warehouse and the application that serve as the data source.
Represents Database system reads current (day to day) transactions within an organization.   Data warehouse system reads historical data for analytical purposes and business reporting.  
Usage ER modeling techniques are used for designing.   Data modeling techniques are used for designing.
Data Storage Approach Flat relational approach method is used for data storage.   Data warehouse uses dimensional and normalized approach for data structure. Example snowflake schema and star.
Speed and Efficiency Due to number of table joins, performing analytical queries is difficult and requires an experienced database administrator familiar with the application to write queries that result in meaningful analysis. In data warehouse, tables are de-normalized to yield and integrated to produce summarized data, multidimensional views and faster query response times.
Data Structure Databases use normalized data structure, whereby data is re-organized so that it does not contain redundant data and all related data items are stored together into multiple tables. Data warehouse uses a de-normalized data structure whereby few tables are used to group information for analytical purposes.
Skilled Personality  A skilled personality or analyst familiar with the application is required to write analytical queries that will eventually result in logical analysis. Structure of data in data warehouse make analytical queries much simpler and flexible hence no skills or firsthand knowledge of the data base is required.
SLA’s The database is directly linked to the front end applications and therefore real-data is always available. SLA’s therefore states that the database must meet 99.9% uptime. In data warehouse are separated from frontend application and therefore SLA’s for data warehouses have downtime built in to accommodate periodic uploads of new data.

Similarities Between Data Warehouse and Database

  • Data warehouse and databases are both relational data systems.
  • They are both data storage systems.
  • Both can be used by multiple users
  • Both can be complex with many tables and large amounts of data.
  • Both can be queried to get answers or pull out data sets based on commonality or exclusion.
  • Databases and data warehouses can be on premise or on cloud-based.